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| //@version=6 | |
| // © QuantPad LLC [made with https://quantpad.ai/] | |
| strategy("'RP Profits' 8AM ORB", | |
| overlay = true, | |
| dynamic_requests = true, | |
| initial_capital = 50000, | |
| default_qty_type = strategy.fixed, | |
| default_qty_value = 2, | |
| commission_type = strategy.commission.cash_per_contract, | |
| commission_value = 1.40, |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| cmake-configure: | |
| rm -rf build && cmake --toolchain toolchain.cmake -S . -B build | |
| cmake-build: | |
| cmake --build build -v |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
- If something goes sideways, STOP and re-plan immediately - don't keep pushing
- Use plan mode for verification steps, not just building
- Write detailed specs upfront to reduce ambiguity
- Use subagents liberally to keep main context window clean
| #!/usr/bin/env bash | |
| ############################################################ | |
| # MIGRATED TO REPOSITORY | |
| # https://github.com/tavinus/cloudsend.sh | |
| # | |
| # This gist will NOT be updated anymore | |
| ############################################################ | |
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